Predicting Ocean Temperature in High-Frequency Internal Wave Area with Physics-Guided Deep Learning: A Case Study from the South China Sea

Abstract

Higher-accuracy long-term ocean temperature prediction plays a critical role in ocean-related research fields and climate forecasting (e.g., oceanic internal waves and mesoscale eddies). The essential component of traditional physics-based numerical models for ocean temperature prediction is solving partial differential equations (PDEs), which has immense challenges in terms of parameterization, initial values, and boundary conditions setting. Moreover, the existing machine learning models for ocean temperature prediction have “black box” problems, and the influence of external dynamic factors is not considered. Moreover, it is hard to judge whether the model satisfies certain physical laws. In this paper, we propose a physics-guided spatio-temporal data analysis model based on the widely used ConvLSTM model to achieve long-term ocean temperature prediction and adopt two schemes to train the model in vector output and multiple parallel input and multi-step output. Meanwhile, considering the spatio-temporal correlation, physical information such as oceanic stable stratification is introduced to guide the model training. We evaluate our proposed approach on several popular deep learning models in different timesteps and data volumes in the northern coast of the South China Sea, where the frequent occurrence of internal waves leads to an intensity trend of a local transformation of sea temperature. The results show higher prediction accuracy compared with the traditional LSTM, and ConvLSTM models, and the introduction of physical laws can improve data utilization while enhancing the physical consistency of the model.